文献类型：期刊文章

作　　者：唐刚[1,2] 马坚伟[3] 杨慧珠[1]

机构地区：[1]清华大学航天航空学院地震波勘探开发研究所,北京100084 [2]中国石油勘探开发研究院石油物探技术研究所,北京100083 [3]哈尔滨工业大学应用数学研究所,哈尔滨150001

出　　处：《Applied Geophysics》

基　　金：supported by The National 973 program (No. 2007 CB209505);Basic Research Project of PetroChina's 12th Five Year Plan (No. 2011A-3601);RIPED Youth Innovation Foundation (No. 2010-A-26-01)

年　　份：2012

卷　　号：9

期　　号：1

起止页码：27-32

语　　种：中文

收录情况：AJ、CSA、CSA-PROQEUST、CSCD、CSCD2011_2012、GEOREFPREVIEWDATABASE、INSPEC、PA、SCI(收录号：WOS:000302365900004)、SCI-EXPANDED(收录号：WOS:000302365900004)、SCIE、SCOPUS、WOS、普通刊

摘　　要：The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.

关 键 词：learning-type overcomplete dictionary  seismic denoising  discrete cosine transform  DATA-DRIVEN

分 类 号：P631.4]